Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges.
Saved in:
| Title: | Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges. |
|---|---|
| Authors: | Nelson, Peter R.1 pnelson@schoodicinstitute.org, Maguire, Andrew J.2 andrew.j.maguire@jpl.nasa.gov, Pierrat, Zoe3, Orcutt, Erica L.4, Yang, Dedi5, Serbin, Shawn5, Frost, Gerald V.6, Macander, Matthew J.6, Magney, Troy S.4, Thompson, David R.2, Wang, Jonathan A.7, Oberbauer, Steven F.8, Zesati, Sergio Vargas9, Davidson, Scott J.10,11, Epstein, Howard E.12, Unger, Steven8, Campbell, Petya K. E.13, Carmon, Nimrod2, Velez‐Reyes, Miguel9, Huemmrich, K. Fred13 |
| Source: | Journal of Geophysical Research. Biogeosciences. Feb2022, Vol. 127 Issue 2, p1-32. 32p. |
| Subject Terms: | *Biomes, *Vegetation dynamics, *Snow cover, Remote sensing, Ecosystem dynamics |
| Abstract: | Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including National Aeronautics and Space Administration's Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low‐stature plants and very fine‐scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long‐term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi‐scale spectroscopy, from lab studies to satellites that enable frequent and continuous long‐term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics. Plain Language Summary: Remote sensing has a long history of characterizing the distribution and dynamics of vegetation in a wide variety of biomes, including the Arctic tundra which is experiencing warming more rapidly than the global average. Imaging spectroscopy (IS)—a rapidly advancing field of remote sensing that measures reflected light in narrow, contiguous "colors" from satellites, aircraft, or towers—has demonstrated great promise to "watch" how key land surface properties vary across space and over time. Because they are vast, remote, and have relatively little infrastructure, currently available IS data from the Arctic tundra are sporadic and intermittent. Hence, it has been challenging to study and characterize these ecosystems across broad spatial scales and through time. Furthermore, the climate and ecology of these ecosystems pose unique challenges for employing and interpreting IS data. Inspired by a forthcoming National Aeronautics and Space Administration satellite‐based IS mission, we present an overview of the current opportunities and challenges for the use of spectroscopy to study Arctic tundra, informed by novel measurements across a range of spatial and temporal scales. We share recommendations for how researchers could leverage IS to resolve pressing ecological questions and advance the design and sampling scheme of future instruments and campaigns. Key Points: Imaging spectroscopy (IS) can help to measure the critical Arctic tundra properties, physiological function, and temporal dynamicsUpcoming IS satellite missions including National Aeronautics and Space Administration's Surface Biology and Geology will make IS data widely available for Arctic tundra regionsTo properly interpret IS data users must consider spectral complexity of tundra driven by composition, sensitivity to climate, and phenology [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Geophysical Research. Biogeosciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: 8gh DbLabel: GreenFILE An: 155435707 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nelson%2C+Peter+R%2E%22">Nelson, Peter R.</searchLink><relatesTo>1</relatesTo><i> pnelson@schoodicinstitute.org</i><br /><searchLink fieldCode="AR" term="%22Maguire%2C+Andrew+J%2E%22">Maguire, Andrew J.</searchLink><relatesTo>2</relatesTo><i> andrew.j.maguire@jpl.nasa.gov</i><br /><searchLink fieldCode="AR" term="%22Pierrat%2C+Zoe%22">Pierrat, Zoe</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Orcutt%2C+Erica+L%2E%22">Orcutt, Erica L.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Yang%2C+Dedi%22">Yang, Dedi</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Serbin%2C+Shawn%22">Serbin, Shawn</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Frost%2C+Gerald+V%2E%22">Frost, Gerald V.</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Macander%2C+Matthew+J%2E%22">Macander, Matthew J.</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Magney%2C+Troy+S%2E%22">Magney, Troy S.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Thompson%2C+David+R%2E%22">Thompson, David R.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jonathan+A%2E%22">Wang, Jonathan A.</searchLink><relatesTo>7</relatesTo><br /><searchLink fieldCode="AR" term="%22Oberbauer%2C+Steven+F%2E%22">Oberbauer, Steven F.</searchLink><relatesTo>8</relatesTo><br /><searchLink fieldCode="AR" term="%22Zesati%2C+Sergio+Vargas%22">Zesati, Sergio Vargas</searchLink><relatesTo>9</relatesTo><br /><searchLink fieldCode="AR" term="%22Davidson%2C+Scott+J%2E%22">Davidson, Scott J.</searchLink><relatesTo>10,11</relatesTo><br /><searchLink fieldCode="AR" term="%22Epstein%2C+Howard+E%2E%22">Epstein, Howard E.</searchLink><relatesTo>12</relatesTo><br /><searchLink fieldCode="AR" term="%22Unger%2C+Steven%22">Unger, Steven</searchLink><relatesTo>8</relatesTo><br /><searchLink fieldCode="AR" term="%22Campbell%2C+Petya+K%2E+E%2E%22">Campbell, Petya K. E.</searchLink><relatesTo>13</relatesTo><br /><searchLink fieldCode="AR" term="%22Carmon%2C+Nimrod%22">Carmon, Nimrod</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Velez‐Reyes%2C+Miguel%22">Velez‐Reyes, Miguel</searchLink><relatesTo>9</relatesTo><br /><searchLink fieldCode="AR" term="%22Huemmrich%2C+K%2E+Fred%22">Huemmrich, K. Fred</searchLink><relatesTo>13</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Geophysical+Research%2E+Biogeosciences%22">Journal of Geophysical Research. Biogeosciences</searchLink>. Feb2022, Vol. 127 Issue 2, p1-32. 32p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Biomes%22">Biomes</searchLink><br />*<searchLink fieldCode="DE" term="%22Vegetation+dynamics%22">Vegetation dynamics</searchLink><br />*<searchLink fieldCode="DE" term="%22Snow+cover%22">Snow cover</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Ecosystem+dynamics%22">Ecosystem dynamics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including National Aeronautics and Space Administration's Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low‐stature plants and very fine‐scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long‐term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi‐scale spectroscopy, from lab studies to satellites that enable frequent and continuous long‐term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics. Plain Language Summary: Remote sensing has a long history of characterizing the distribution and dynamics of vegetation in a wide variety of biomes, including the Arctic tundra which is experiencing warming more rapidly than the global average. Imaging spectroscopy (IS)—a rapidly advancing field of remote sensing that measures reflected light in narrow, contiguous "colors" from satellites, aircraft, or towers—has demonstrated great promise to "watch" how key land surface properties vary across space and over time. Because they are vast, remote, and have relatively little infrastructure, currently available IS data from the Arctic tundra are sporadic and intermittent. Hence, it has been challenging to study and characterize these ecosystems across broad spatial scales and through time. Furthermore, the climate and ecology of these ecosystems pose unique challenges for employing and interpreting IS data. Inspired by a forthcoming National Aeronautics and Space Administration satellite‐based IS mission, we present an overview of the current opportunities and challenges for the use of spectroscopy to study Arctic tundra, informed by novel measurements across a range of spatial and temporal scales. We share recommendations for how researchers could leverage IS to resolve pressing ecological questions and advance the design and sampling scheme of future instruments and campaigns. Key Points: Imaging spectroscopy (IS) can help to measure the critical Arctic tundra properties, physiological function, and temporal dynamicsUpcoming IS satellite missions including National Aeronautics and Space Administration's Surface Biology and Geology will make IS data widely available for Arctic tundra regionsTo properly interpret IS data users must consider spectral complexity of tundra driven by composition, sensitivity to climate, and phenology [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Geophysical Research. Biogeosciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=155435707 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1029/2021JG006697 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 1 Subjects: – SubjectFull: Biomes Type: general – SubjectFull: Vegetation dynamics Type: general – SubjectFull: Snow cover Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Ecosystem dynamics Type: general Titles: – TitleFull: Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nelson, Peter R. – PersonEntity: Name: NameFull: Maguire, Andrew J. – PersonEntity: Name: NameFull: Pierrat, Zoe – PersonEntity: Name: NameFull: Orcutt, Erica L. – PersonEntity: Name: NameFull: Yang, Dedi – PersonEntity: Name: NameFull: Serbin, Shawn – PersonEntity: Name: NameFull: Frost, Gerald V. – PersonEntity: Name: NameFull: Macander, Matthew J. – PersonEntity: Name: NameFull: Magney, Troy S. – PersonEntity: Name: NameFull: Thompson, David R. – PersonEntity: Name: NameFull: Wang, Jonathan A. – PersonEntity: Name: NameFull: Oberbauer, Steven F. – PersonEntity: Name: NameFull: Zesati, Sergio Vargas – PersonEntity: Name: NameFull: Davidson, Scott J. – PersonEntity: Name: NameFull: Epstein, Howard E. – PersonEntity: Name: NameFull: Unger, Steven – PersonEntity: Name: NameFull: Campbell, Petya K. E. – PersonEntity: Name: NameFull: Carmon, Nimrod – PersonEntity: Name: NameFull: Velez‐Reyes, Miguel – PersonEntity: Name: NameFull: Huemmrich, K. Fred IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 21698953 Numbering: – Type: volume Value: 127 – Type: issue Value: 2 Titles: – TitleFull: Journal of Geophysical Research. Biogeosciences Type: main |
| ResultId | 1 |
